Powering FDA-qualified kidney safety biomarker research with secure data sharing—supporting over 26,600 trial participants and transforming drug development decision-making.
Qualifying a novel biomarker for regulatory use requires large, diverse, and well-governed datasets. But the data that would power qualification is scattered across dozens of independent studies, held by competing sponsors, and subject to complex data protection obligations.
Whether the goal is safety biomarker qualification, companion diagnostic development, or disease progression monitoring, organisations face the same structural barriers: fragmented data, mismatched standards, and governance frameworks that were never designed for multi-party collaboration. Without a purpose-built platform, the burden of building a repository falls on the science team — consuming resource that should be focused on analysis.
Patient-level biomarker data is distributed across independent clinical trials, nonclinical studies, and registries. Without centralisation, datasets are too small to be reliably predictive for regulatory submissions.
Studies contribute data in incompatible formats and terminologies. Without rigorous harmonisation to standards like CDISC, SDTM or OMOP, cross-study analysis is unreliable — or impossible.
Managing data contributor agreements, access controls, and usage conditions across pharmaceutical companies, academic institutions, and regulators requires infrastructure most consortia don’t have.
Sponsors need confidence that contributing to a shared repository does not expose proprietary study data or prejudice ongoing regulatory submissions. Governance architecture must protect all contributors.
The Aridhia DRE provides the secure, scalable infrastructure to build and operate a biomarker data repository — from initial data contribution through standardisation, governed access, and regulatory-grade analytics — without requiring sponsors to cede control of their data.
The platform handles the infrastructure complexity so research teams can focus on the science. Data contributors retain ownership of their submissions, and the platform enforces the governance rules that make collaboration safe and compliant.
Ingest patient-level biomarker data from clinical trials, nonclinical studies, observational registries, and real-world data sources. Flexible data ingest and indexing supports diverse submission formats while preserving provenance at every step.
Contributed dataset require harmonisation and validation to interoperable data standards — CDISC SDTM, OMOP, or consortium-specific models — ensuring consistency and comparability across studies and sponsors.
FAIR Data Services provides a searchable data and asset catalogue with integrated data access request (DAR) workflows. Administrators manage contributor agreements, usage conditions, and role-based permissions — all audited and traceable.
Native R, Python, and SQL environments sit alongside Git versioning and support for specialised PK/PD modelling tools. AI/ML algorithms and validated statistical methods run within the same secure, audited environment used for regulatory-grade submissions.
The offline AIRA framework enables LLM-assisted code generation, model development, and data analysis without any data leaving the secure boundary. No external API calls unless enabled, and audit trails of every AI interaction for reproducibility and compliance.
The open-source Federated Node enables analytical tasks to run against data held at partner sites without requiring data movement. Supporting both cross-site benchmarking and federated ML model training across institutional boundaries.
Vector search in FAIR Data Services enables semantic discovery of datasets based on clinical meaning rather than exact terminology, surfacing relevant data even when study vocabulary varies across contributors.
Outbound airlock controls enforce disclosure control and data conditions compliance before any result leaves the secure environment, providing the governance assurance required by data contributors and regulators alike.
Azure cloud-based architecture with flexible compute, unlimited storage, and ISO 27001 + HITRUST certified security. Capable of scaling from a pilot repository of a few studies to a global consortium programme serving hundreds of users.
Critical Path Institute’s Predictive Safety Testing Consortium’s (PSTC) Biomarker Data Repository (BmDR) illustrates what becomes possible for multi-sponsor scientific collaboration.
Beginning with regulatory consultations in 2007, FDA qualification of a six-biomarker clinical kidney safety panel in 2018, and migration of data to the Aridhia DRE platform in 2024, the BmDR program demonstrates the full lifecycle that a biomarker repository platform must support. The platform supports data from nonclinical phase to clinical trials used in active drug development programs worldwide, as well as investigator-initiated studies in disease populations of interest for evaluating emerging safety biomarkers.
Critical Path Institute’s Predictive Safety Testing Consortium (PSTC) brought together pharmaceutical sponsors, academic researchers, and regulators to address a major challenge in drug development. The challenge at hand was to take traditional clinical biomarkers for drug-induced kidney injury (DIKI), such as serum creatinine and estimated GFR, and make them more detectable. Often kidney damage is detected only after significant injury has already occurred. This delay can limit early intervention and contribute to costly late-stage drug failures.
To address this, PSTC set out to aggregate nonclinical studies, patient-level data from clinical trials, and observational clinical studies contributed by various partners globally. Rigorous standardization and curation processes ensured that cross-study analyses are possible, and the resulting qualification package met the evidentiary standards of the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), and Japan’s Pharmaceuticals and Medical Devices Agency (PMDA). PSTC is preparing a qualification submission for an extended panel of kidney safety biomarkers to detect DIKI more sensitively and specifically at an individual-patient level (as opposed to a cohort level). The panel qualification may accelerate development decisions for novel therapeutics, and precision medicine test development to more robustly detect and characterize injury onset and recovery patterns.
Now hosted on the Aridhia DRE, C-Path’s biomarker data and analytics platform supports multi-sponsor data contribution under strict governance, combined with integrated analytics for regulatory-grade submissions. The qualified panel of six urinary biomarkers is now incorporated into active drug development programs across the industry — enabling more sensitive safety monitoring and better-informed go/no-go decisions (Zabka et. al. 2025).
What the BmDR programme demonstrates: Pharmaceutical sponsors have used the qualified biomarker panel to support both advancement and early termination decisions — effectively de-risking and improving clinical trial success (Zabka et. al. 2025).
It is the intention of BmDR to disseminate understanding of emerging safety biomarkers by providing access to completed de-identified studies in different diseases, demographics, and approved therapies with known toxicity risks (such as drug-induced kidney injury) to researchers.
To learn more about the BmDR : c-path.org/BmDR To get involved reach out to kperon@c-path.org
The same infrastructure that supports organ safety biomarker qualification applies across a wide range of biomarker programme types. The Aridhia DRE is designed to adapt to the specific governance, standardisation, and analytical requirements of each.
| Programme Type | Key Platform Requirements | Representative Outcome |
|---|---|---|
| Organ Safety Biomarker Qualification | Multi-sponsor ingestion, CDISC data standards, regulatory-grade audit trails | Qualification dossier supporting regulatory submission (eg FDA, EMA, MHRA) |
| Companion Diagnostic Development | Patient stratification analytics, genomic data integration, GDPR-compliant sharing | Validated companion diagnostic aligned to therapeutic indication |
| Disease Progression Biomarkers | Repository for longitudinal patient registry, real-world data, federated multi-site access | Prognostic model informing trial design, disease progression, and endpoint selection |
| Pharmacodynamic Biomarker Programmes | PK/PD modelling integration, cross-study comparability, MIPD tools, custom tooling deployment | Target engagement evidence supporting dose selection and trial simulation tools |
| Multi-Organ Safety Expansion | Modular repository architecture, shared analytical methods, incremental contributor onboarding | Consolidated safety repository covering liver, kidney, cardiac, and skeletal muscle endpoints |
When biomarker data is properly aggregated, standardised, and governed, it changes what is scientifically and commercially possible — for the consortium, for individual sponsors, and ultimately for patients.
Novel biomarkers qualified through sufficient aggregate data detect injury significantly earlier than standard-of-care measures, enabling dose cohort risk assessment before irreversible damage occurs.
More sensitive safety monitoring allows sponsors to make evidence-based advancement and termination decisions earlier in development, reducing costly late-stage failures and unnecessary patient exposure.
Validated clinical biomarker data that translates robustly from nonclinical models supports decisions to reduce or refine animal studies, with the repository providing the cross-species evidence base required.
A centrally governed, auditable repository with validated analytical methods produces submissions that meet the evidentiary standards of FDA, EMA, and other global regulators, with provenance and reproducibility built in.
Integrated multi-sponsor data enables identification of signals across diverse populations, dose regimens, and therapeutic areas that no individual study could reveal in isolation.
Infrastructure built for one biomarker programme can be extended to new endpoints, organ systems, or disease areas, compounding the value of the initial investment across the consortium’s research.
Aridhia has supported some of the most demanding data collaboration programmes in life sciences and healthcare — from the C-Path BmDR to rare disease research platforms for the FDA-funded RDCA-DAP initiative and international COVID-19 research infrastructure for the ICODA consortium. We bring deep operational expertise in deploying and sustaining secure environments for programmes where data governance is not optional.
Deployed across global consortia supporting 84,600+ patient observations. Production infrastructure, not a proof of concept.
ISO 27001 + HITRSUT certified with established track record supporting FDA, EMA, MHRA, and PMDA qualification programmes.
Open-source Federated Node for cross-institutional analysis so data stays where it is, while results come to you.
Built-in data access request workflows, usage conditions, and FAIR metadata catalogue so governance scales with your consortium.
Whether you are launching a new consortium programme, expanding an existing repository, or exploring federated approaches to multi-site biomarker research — speak to our team.
